Buggy code:
public class testParentChild_Gibbs { public static void main ( String [ ] args ) throws IOException , ParseException { int classNumber = 5 ; int Ngram = 1 ; String featureValue = "TF" ; int norm = 0 ; int lengthThreshold = 5 ; int minimunNumberofSentence = 2 ; <START_BUG> String topicmodel = "LDA_GibbsParentChild" ; <END_BUG> String category = "tablet" ; int number_of_topics = 20 ; boolean loadNewEggInTrain = true ; boolean setRandomFold = false ; int loadAspectSentiPrior = 0 ; double alpha = 1.0 + 0.01 ; double beta = 1.0 + 0.001 ; double eta = ( topicmodel . equals ( "LDA_Gibbs" ) ) ? 200 : 5.0 ; double converge = 1.0E-9 ; double lambda = 0.9 ; int varIter = 10 ; double varConverge = 1.0E-5 ; int topK = 10 ; int number_of_iteration = 50 ; int crossV = 0 ; int gibbs_iteration = 4 ; int gibbs_lag = 2 ; double burnIn = 0.4 ; boolean display = true ; boolean sentence = false ; String [ ] tabletProductList = new String [ ] { "B008GFRDL0" } ; String [ ] cameraProductList = new String [ ] { "B005IHAIMA" } ; String [ ] phoneProductList = new String [ ] { "B00COYOAYW" } ; String [ ] tvProductList = new String [ ] { "B0074FGLUM" } ; String articleType = "ArsTech" ; String yahooNewsFolder = ".//data//AT-YahooArticles" ; String yahooCommentsFolder = ".//data//AT-YahooComments" ; String TechArticlesFolder = ".//data//ParentChildTopicModel//ArsTechnicaArticles" ; String TechCommentsFolder = ".//data//ParentChildTopicModel//ArsTechnicaComments" ; String suffix = ".json" ; String tokenModel = ".//data//Model//en-token.bin" ; String stnModel = null ; String posModel = null ; if ( ( ( ( topicmodel . equals ( "HTMM" ) ) || ( topicmodel . equals ( "LRHTMM" ) ) ) || ( topicmodel . equals ( "HTSM" ) ) ) || ( topicmodel . equals ( "LRHTSM" ) ) ) { stnModel = ".//data//Model//en-sent.bin" ; posModel = ".//data//Model//en-pos-maxent.bin" ; sentence = true ; } String fvFile = String . format ( ".//data//Features//fv_%dgram_topicmodel_%s.txt" , Ngram , articleType ) ; String fvStatFile = String . format ( ".//data//Features//fv_%dgram_stat_topicmodel.txt" , Ngram ) ; String aspectList = ( ".//data//Model//aspect_" + category ) + ".txt" ; String aspectSentiList = ( ".//data//Model//aspect_sentiment_" + category ) + ".txt" ; String pathToPosWords = ".//data//Model//SentiWordsPos.txt" ; String pathToNegWords = ".//data//Model//SentiWordsNeg.txt" ; String pathToNegationWords = ".//data//Model//negation_words.txt" ; String pathToSentiWordNet = ".//data//Model//SentiWordNet_3.0.0_20130122.txt" ; LocalTime today = LocalTime . now ( ) ; File resultRootFolder = new File ( ".//data//results" ) ; if ( ! ( resultRootFolder . exists ( ) ) ) { System . out . println ( ( "creating<seq2seq4repair_space>directory" + resultRootFolder ) ) ; resultRootFolder . mkdir ( ) ; } String filePrefix = ( ( ".//data//results//" + ( today . getHour ( ) ) ) + ( today . getMinute ( ) ) ) + topicmodel ; File resultFolder = new File ( filePrefix ) ; if ( ! ( resultFolder . exists ( ) ) ) { System . out . println ( ( "creating<seq2seq4repair_space>directory" + resultFolder ) ) ; resultFolder . mkdir ( ) ; } String infoFilePath = filePrefix + "//information.txt" ; System . out . println ( "Creating<seq2seq4repair_space>feature<seq2seq4repair_space>vectors,<seq2seq4repair_space>wait..." ) ; ParentChildAnalyzerwithStn analyzer = new ParentChildAnalyzerwithStn ( tokenModel , classNumber , fvFile , Ngram , lengthThreshold ) ; analyzer . LoadParentDirectory ( TechArticlesFolder , suffix ) ; analyzer . LoadChildDirectory ( TechCommentsFolder , suffix ) ; analyzer . setFeatureValues ( featureValue , norm ) ; _Corpus c = analyzer . returnCorpus ( fvStatFile ) ; if ( topicmodel . equals ( "2topic" ) ) { twoTopic model = new twoTopic ( number_of_iteration , converge , beta , c , lambda , analyzer . getBackgroundProb ( ) ) ; if ( crossV <= 1 ) { for ( _Doc d : c . getCollection ( ) ) { model . inference ( d ) ; model . printTopWords ( topK ) ; } } else model . crossValidation ( crossV ) ; } else if ( topicmodel . equals ( "Tensor" ) ) { c . saveAs3WayTensor ( ".//data//vectors//3way_tensor.txt" ) ; } else { pLSA model = null ; if ( topicmodel . equals ( "pLSA" ) ) { model = new topicmodels . multithreads . pLSA_multithread ( number_of_iteration , converge , beta , c , lambda , number_of_topics , alpha ) ; } else if ( topicmodel . equals ( "LDA_Gibbs" ) ) { model = new LDA_Gibbs ( gibbs_iteration , 0 , beta , c , lambda , number_of_topics , alpha , burnIn , gibbs_lag ) ; } else if ( topicmodel . equals ( "LDA_Variational" ) ) { model = new topicmodels . multithreads . LDA_Variational_multithread ( number_of_iteration , converge , beta , c , lambda , number_of_topics , alpha , varIter , varConverge ) ; } else if ( topicmodel . equals ( "HTMM" ) ) { model = new HTMM ( number_of_iteration , converge , beta , c , number_of_topics , alpha ) ; } else if ( topicmodel . equals ( "HTSM" ) ) { model = new HTSM ( number_of_iteration , converge , beta , c , number_of_topics , alpha ) ; } else if ( topicmodel . equals ( "LRHTMM" ) ) { model = new LRHTMM ( number_of_iteration , converge , beta , c , number_of_topics , alpha , lambda ) ; } else if ( topicmodel . equals ( "LRHTSM" ) ) { model = new LRHTSM ( number_of_iteration , converge , beta , c , number_of_topics , alpha , lambda ) ; } else if ( topicmodel . equals ( "CTM_Variational" ) ) { } else if ( topicmodel . equals ( "ParentChild_Gibbs" ) ) { alpha = alpha - 1 ; double mu = 0.001 ; double [ ] gamma = new double [ 2 ] ; gamma [ 0 ] = 2 ; gamma [ 1 ] = 2 ;

Fix:
String topicmodel = "ParentChild_Gibbs" ;